Ornith-1.0-35B-6bit-XL-mlx

Flight Simulator — Coded by This Model

A real-world coding benchmark: each model was prompted to write a complete flight simulator from scratch. The resulting code was rendered and recorded.

About XL Quantization

BaseQuant_XL is a fully data-agnostic, static quantization. No calibration dataset, no sensitivity analysis, no importance matrix. Precision is allocated purely by architectural role — routing-critical layers get higher precision, bulk expert parameters get lower precision. The result is a transparent, faithful capture of the source model.

Data-dependent calibration quantizations (iMatrix, AWQ, GPTQ, oQ, oQ4e, etc.) use a calibration set to guide bit allocation. This can produce a skewed representation of the model: domains well-represented in the calibration data (English, popular topics, public or leaked benchmarks) are preserved better, while underrepresented domains (non-English languages, niche use cases, your own data) are preserved worse. XL avoids this trade-off entirely — it generalizes honestly because it is never fit to any particular data distribution.

Local SOTA for 48GB Macs — Intelligence Benchmark Comparison

This model is part of a benchmark comparison of the best local MLX-quantized LLMs that fit in 48GB unified memory on Apple Silicon. All benchmarks run in instruct mode (no thinking) with n=50 samples per benchmark.

Collection: Local SOTA for 48GB Macs

⚠️ n=50 sampling means wide confidence intervals (±~13% at 95% CI). Differences under ~6 points may not be statistically significant. Models using data-aware quantization (oQ/oQe) may be calibrated on benchmark-like data — their scores carry a benchmaxxing caveat. The BaseQuant_XL variants (data-agnostic) provide the most honest generalization estimates.

leonsarmiento/Ornith-1.0-35B-6bit-XL-mlx

This model was converted to MLX format from deepreinforce-ai/Ornith-1.0-35B using BaseQuant_XL 6/8-bit mixed quantization optimized for Apple Silicon. The vision encoder is preserved and quantized at 6-bit, making this a full multimodal model.

BaseQuant_XL keeps the most routing-critical layers in full bf16 precision — the MoE router gate, shared expert gate, shared expert, and lm_head — while applying aggressive quantization to the bulk parameters. This preserves routing accuracy and output quality where it matters most.

Ornith-1.0-35B is a 35B-parameter MoE (Mixture of Experts) model fine-tuned from Qwen3.5-35B-A3B by DeepReinforce AI, using a self-improving RL training framework that jointly optimizes scaffold and solution rollouts for agentic coding tasks. Despite 35B total parameters, only ~3B are activated per token. It features 256 experts (8 active per token + 1 shared expert), hybrid full + linear (Gated DeltaNet) attention, and a vision encoder.

Benchmark Highlights

Use with mlx

pip install -U mlx-vlm

python -m mlx_vlm.generate --model leonsarmiento/Ornith-1.0-35B-6bit-XL-mlx --max-tokens 256 --temperature 1.0 --top-p 1.0 --prompt "Hello"

BaseQuant_XL Quantization Strategy

Quantization Details

• Quantization type: BaseQuant_XL mixed (multimodal, vision preserved)
• Bits per weight: 6.808
• Total size: ~28 GB (6 shards)
• Group size: 64
• Method: Custom quant_predicate via mlx_vlm

Recommended Inference Parameters

Note: Ornith-1.0-35B uses Temp 1.0 and Top_p 1.0 per the model's Terminal-Bench 2.1 benchmark recipe. This is a Qwen3.5-based model — preserve_thinking is not applicable.